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Geostatistical Software for Non-Parametric Geostatistical Modeling of Uncertainty

Award Number: R43LM014519
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/15/2023
PERIOD OF PERFORMANCE END DATE: 02/29/2024

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Geostatistical Software for Non-Parametric Geostatistical Modeling of Uncertainty - 7. Project Summary/Abstract A key component in any investigation of association and/or cause-effect relationships between the environment and health outcomes is the availability of accurate and precise models of exposure. Because the cost of collecting field data is often prohibitive, it is critical to incorporate any source of secondary information available to supplement sparse datasets. Secondary data can take many forms (e.g., continuous or categorical measurement scale), and display different levels of reliability: hard vs soft data (e.g., interval-type data, probability distributions). Merging these different data layers while accounting for their spatial patterns, compositional nature (case of categorical attributes) and local uncertainty is thus challenging. This SBIR project is developing the first commercial software to offer tools for soft indicator coding and non- parametric geostatistical modeling of uncertainty. The research product will be a stand-alone desktop space- time (ST) analysis and visualization tool, building on the legacy core software developed by BioMedware. These tools will be suited for the analysis of data outside health sciences, such as in remote sensing, geochemistry, urban infrastructure or soil science, broadening significantly the commercial market for the end product. This project will accomplish four aims:  Develop an indicator kriging alternative to Poisson and binomial kriging for filtering noise caused by the small number problem and to disaggregate areal rate data (Area-to-Point kriging), while avoiding the generation of negative kriging estimates.  Implement simplicial indicator kriging for predicting the probability of occurrence of categorical data and, using the case of the composition of service lines (SL) in Flint Michigan, compare the accuracy of this compositional approach to: 1) traditional indicator kriging that can result in negative probabilities of occurrence and probabilities that do not sum to one, and 2) a combination of machine learning and Bayesian data analysis used by BlueConduit, a US leader in SL composition prediction.  Develop and test a prototype module that will guide non-expert through the soft indicator coding of information and variogram modeling, followed by the spatial interpolation and cross-validation based on BioMedware’s space-time visualization and analysis technology.  Conduct a usability and user experience study and identify additional methods and tools to consider in Phase II. These technologic, scientific and commercial innovations will enhance our ability to model geostatistically multivariate space-time phenomena and compute estimates and the associated uncertainty at the scale (e.g. point location, census-tract level) the most relevant for environmental epidemiology.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $0 )
2024	2023	BIO-ANALYTICS, INC.	330 E LIBERTY ST	ANN ARBOR	MI	48104	WASHTENAW	USA	Medical Library Assistance	000	1	6/21/2024	NEW	$0
														Subtotal = $0

Issue Date FY: 2023 ( Subtotal = $299,849 )
2023	2023	BIO-ANALYTICS, INC.	167 LITTLE LAKE DR	ANN ARBOR	MI	48103	WASHTENAW	USA	Medical Library Assistance	000	1	9/13/2023	NEW	$299,849
														Subtotal = $299,849

Grand Total All Awards = $299,849

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Geostatistical Software for Non-Parametric Geostatistical Modeling of Uncertainty

Award Number: R43LM014519

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/15/2023

PERIOD OF PERFORMANCE END DATE: 02/29/2024

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